Foundation Model Integration for Cold-Start Active Learning

Core Concepts

Integrating foundation models with clustering improves cold-start active learning.

Abstract

Active learning selects informative samples within a limited annotation budget. Previous studies lack focus on selecting samples for cold-start model initialization. Foundation models generate low-dimensional embeddings for clustering. Experiments on clinical tasks show enhanced performance with foundation model-based clustering. Proposed method offers an effective paradigm for future cold-start active learning.

Stats

Random sampling prone to fluctuation. Naive clustering faces convergence challenges with high-dimensional data. Foundation models generate informative embeddings for clustering.

Quotes

"Foundation models refer to those trained on massive datasets by the self-supervised paradigm." "Experiments on two clinical tasks demonstrated enhanced performance with foundation model-based clustering."

Key Insights Distilled From

Foundation Model Makes Clustering A Better Initialization For Cold-Start Active Learning

by Han Yuan,Chu... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2402.02561.pdf

Deeper Inquiries

How can fluctuation in evaluation metrics be mitigated in active learning?

Fluctuation in evaluation metrics in active learning can be mitigated through several strategies. One approach is to increase the dataset size to stabilize parameter estimation and reduce the impact of outliers. By having a larger dataset, the model can learn more robust patterns and reduce the influence of noise. Additionally, conducting multiple simulations with different subsets of the data can help in stabilizing the evaluation metrics by averaging out the fluctuations observed in individual runs. Another strategy is to carefully select the validation set and ensure that it is representative of the overall dataset. By having a well-balanced validation set, the model's performance can be more accurately assessed, reducing the variability in evaluation metrics. Moreover, using more sophisticated optimization techniques, such as adaptive learning rates or regularization methods, can help in preventing the model from overfitting to noisy data points, thereby reducing fluctuations in evaluation metrics.

How does the specialization of foundation models impact their performance in different tasks?

The specialization of foundation models can significantly impact their performance in different tasks. Foundation models trained on specific domains or datasets tend to excel in tasks related to those domains due to their specialized knowledge and feature representations. For example, in the context of medical imaging, foundation models like TorchXRayVision (TXRV) and CXR Foundation (CXRF) are trained on chest radiographs and thoracic diseases, making them highly effective for tasks related to diagnostic imaging in the chest area. On the other hand, a more generalist foundation model like REMEDIS, which covers multiple medical imaging domains, may not perform as well in specific tasks compared to specialized models. The generalist model may lack the domain-specific knowledge and fine-tuned features that are crucial for achieving high performance in specialized tasks. Therefore, the specialization of foundation models plays a crucial role in determining their performance across different tasks, with specialized models often outperforming generalist models in domain-specific tasks.

How can the proposed method be applied to other domains beyond medical imaging?

The proposed method of integrating foundation models with clustering for cold-start active learning initialization can be applied to various domains beyond medical imaging. The key idea of leveraging informative embeddings generated by foundation models to improve sample selection for model initialization is a versatile concept that can be adapted to different domains and tasks. For example, in natural language processing, foundation models like BERT or GPT trained on large text corpora can be used to generate embeddings for text data. These embeddings can then be utilized in conjunction with clustering algorithms to select informative samples for initializing active learning tasks in text classification or sentiment analysis. Similarly, in computer vision tasks such as object detection or image segmentation, foundation models trained on diverse image datasets can provide rich embeddings for images, which can be leveraged for clustering-based sample selection in cold-start active learning scenarios. Overall, the proposed method's flexibility and effectiveness in improving sample selection can be harnessed across various domains beyond medical imaging, making it a valuable approach for enhancing active learning in different fields.